Two general strategies exist for computing the distances between mentions which are needed for clustering: representation learning and metric learning [1]}. Representation learning approaches produce a vector representation for each event mention independently. The final event clustering is obtained by computing the cosine distance between each vector pair, followed by agglomerative clustering on the resulting distance matrix.
Most approaches belong to the group of conceptually simpler metric learners which predict the semantic distance between two mentions or clusters based on a set of features. By applying the metric on all \(\binom{n}{2}\)  pairs for \(n\)  mentions, a distance matrix is obtained which is then fed to a clustering algorithm. Any probabilistic classifier or regression model may be used to obtain the mention distances.
Metric learning models can be further divided into mention pair models which compute the distance between each mention pair once and cluster pair models which recompute cluster representations and distances after each cluster merge.
Computing the distance between all mention pairs can be a computationally expensive process. Some metric learner models therefore perform a separate document preclustering step to break down the task into manageable parts. The metric learning approach is then applied on each individual cluster of documents and its results are combined to produce the final coreference clustering.
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